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Secondary Title
International Journal of Advanced Engineering Technologies and Innovations
Abstract

In the contemporary digital landscape, the convergence of artificial intelligence (AI) and big data technologies holds immense promise for revolutionizing cybersecurity practices and fortifying future networks against evolving threats. This paper explores the synergistic potential of AI and big data in creating robust cybersecurity ecosystems capable of effectively mitigating cyber risks and safeguarding critical infrastructures. Through a comprehensive analysis of existing literature and emerging trends, this study elucidates the transformative impact of AI and big data integration in enhancing threat detection, incident response, and risk management strategies. By harnessing the power of AI algorithms for real-time threat analysis and leveraging big data analytics for contextual insights and anomaly detection, organizations can proactively identify and mitigate cyber threats before they escalate into full-blown attacks. The methodology involves a systematic review of relevant literature, followed by a critical analysis of key findings and implications for future research and practice.

Concluding remarks
The results highlight the multifaceted benefits of synergizing AI and big data in bolstering cybersecurity defenses, including improved detection accuracy, reduced response times, and enhanced situational awareness. Furthermore, the discussion explores the challenges and opportunities associated with AI and big data integration, such as data privacy concerns, scalability issues, and the need for interdisciplinary collaboration. By addressing these challenges and leveraging the complementary strengths of AI and big data technologies, organizations can build resilient cybersecurity ecosystems capable of adapting to the dynamic threat landscape of future networks. In conclusion, this paper advocates for a paradigm shift
towards AI-driven, data-centric cybersecurity approaches, emphasizing the importance of collaboration, innovation, and continuous learning in building robust defenses for the digital age.

Reference details

Resource type
Miscellaneous
Year of Publication
2022
Publication Area
Cybersecurity and defense

How to cite this reference:

Cybersecurity Threat Landscape: Predictive Modelling Using Advanced AI Algorithms. (2022). Retrieved from https://ijaeti.com/index.php/Journal/article/download/318/330